Region-based convolutional neural network (R-CNN) [1]} was among the first works on object detection using a CNN.
This algorithm uses the selective search [2]} algorithm to generate bounding box proposals, and then crops an image patch using each proposal.
The image patches are subsequently scaled and run through a CNN model for object detection.
This approach achieved 53.7% accuracy on the PASCAL VOC dataset.
R-CNN is extremely slow (13 seconds per image) because all patches need to run through the CNN individually.
